Types and basic structures data in R презентация

The purpose of the lecture is to familiarize yourself with the basic data types used in the R language, as well as with the basic structures that the R language operates on.
As a result of studying the lecture materials, you will know how to create data of various types, as well as operate on the main data structures.
Statistical programming languages

1. Data types in R
2. Basic data structures:
2.1 Vectors
2.2 Matrices
2.3 Arrays
2.4 Frames
2.5 Factors
2.6 Lists
Lecture questions
Statistical programming languages

Visual statistics. We use R! A. B. Shipunov, E. M. Baldin, P. A. Volkova, A. I. Korobeinikov, S. A. Nazarova, S. V. Petrov, V. G. Sufiyanov. 2014 year
Introduction to R: Notes on R: a programming environment for analyzing data and graphics. Version 3.1.0 (2014-04-10) U.N. Venables, D.M. Smith., Translation from English. - Moscow, 2014.109 s. - (series of technical documentation).
Statistical analysis and data visualization using R. S.E. Mastitsky, V.K. Shitikov, Heidelberg - London - Tolyatti, 2014.401 p. Website: http://r-analytics.blogspot.co Website: http://www.qsar4u.com/files/rintro/01.html
Statistical programming languages
Literary source :

2. Data Types in R
Structured and unstructured
Clean and dirty
Numerical, classification
Symbols, text, pictures, speech
80% of the work is collecting and cleaning data !
Big data is usually BIG and unstructured
Statistical programming languages

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2. Data Types in R

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• Retrieving Data Type Information :
>class (x)
• Type Verification :
>is.[type] (x)
>is.list(x)
• Type cast :
>as.[type] (x)
>as. numeric(x)
2. Data Types in R
>class(present$year)
[1] "numeric"
>is.logical(present$year)
[1] FALSE
>as.factor(present$year)
[1] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 [16] 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 [31] 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 [46] 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 [61] 2000 2001 2002
63 Levels: 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 ... 2002

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MISSING VALUES - NA
NA test::
>is.na (x)
Getting rid of NA::
>na.omit (data)
2. Data Types in R
Often it is not possible to collect all the data on all parameters of the objects of interest to us.
Missing (unknown) observation values ​​are indicated in R as NA (“Not available”).

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2. Data Types in R
nominal
continuous
ordered
discrete
Define the data types for the columns of this table:

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3. Basic data structures

Statistical programming languages
3. Basic data structures

Statistical programming languages
3. Basic data structures

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3. Features of the data structure in R
an R object is everything that can be represented in the form of variables, including constants, various data types, functions, and even diagrams.
Objects have: view (determines in what form the object is stored in memory) and a class (which tells common functions of type print how to handle it).
Columns are variables, and rows are observations. Variable types of variables can be contained in one data table. Data tables are the main type of data structure.
Factors are nominal or ordinal variables. In R, they are stored and processed in a special way.
A data frame is a type of data structure in R that is similar to the type in which data is stored in ordinary statistical programs (in SAS, SPSS and STATA).

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3. Basic data structures: vectors
Vectors are vector data arrays that can contain numeric, textual, or logical data. To create a vector, the union function c () is used.:
a <- c(1, 2, 5, 3, 6, -2, 4)
b <- c("one", "two", "three")
c <- c(TRUE, TRUE, TRUE, FALSE, TRUE, FALSE)

Statistical programming languages
3. Basic data structures: vectors
Individual elements of a vector can be called using a numerical vector consisting of element numbers in square brackets. For example, a [c (2, 4)] denotes the second and fourth elements of the vector.
a <- c(1, 2, 5, 3, 6, -2, 4)
a[3]
[1] 5
a[c(1, 3, 5)] [1] 1 5 6
a[2:6]
2 5 3 6 -2
The colon in the last example is used to create a sequence of numbers..
a <- c(2:6) is the same as a <- c(2, 3, 4, 5, 6).

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3. Basic data structures: matrices
A matrix is a two-dimensional data array in which each element has the same type (numeric, textual, or logical). Common format :
mymatrix <- matrix(vector, nrow=number_of_rows, ncol=number_of_columns,
byrow=logical_value, dimnames=list(
char_vector_rownames, char_vector_colnames))
where vector contains elements of the matrix, nrow and ncol define the number of rows and columns in the matrix, and dimnames contains the names of rows and columns, which are stored as text vectors (they do not need to be specified). The byrow parameter determines whether the matrix should be filled by rows (byrow=TRUE) or by columns (by row=FALSE). By default, the matrix is populated by columns.

Statistical programming languages
3. Basic data structures: matrices
Program code. Matrix Creation
y <- matrix(1:20, nrow=5, ncol=4)
y
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 2 7 12 17
[3,] 3 8 13 18
[4,] 4 9 14 19
[5,] 5 10 15 20

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3. Basic data structures: matrices
> cells <- c(1,26,24,68)
>rnames <-c("R1", "R2")
>cnames <-c("C1", "C2")
mymatrix <- matrix(cells, nrow=2, ncol=2, byrow=TRUE, dimnames=list(rnames, cnames))
mymatrix # 2 × 2 table filled in rows
C1 C2
R1 1 26
R2 24 68
> mymatrix <- matrix(cells, nrow=2, ncol=2, byrow=FALSE, dimnames=list(rnames, cnames)) Ž
> mymatrix # 2 × 2 table filled in columns
C1 C2
R1 1 24
R2 26 68

Statistical programming languages
3. Basic data structures: matrices
Using indexes when working with matrices
> x <- matrix(1:10, nrow=2)
> x
[,1] [,2] [,3] [,4] [,5]
[1,] 1 3 5 7 9
[2,] 2 4 6 8 10
> x[2,] # display the 2nd row of the matrix
[1] 2 4 6 8 10
> x[,2] # display the 2nd column of the matrix
[1] 3 4
> x[1,4] # derive a matrix element from the 1st row and 4th column
[1] 7
> x[1, c(4,5)] # to display the matrix elements of the 1st row, 4-th and 5-th column
[1] 7 9

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3. Basic data structures: arrays
Arrays are similar to matrices, but can have more than two dimensions.
myarray <- array(vector, dimensions, dimnames)
where vector contains the data itself, dimensions is a numeric vector specifying the dimension for each dimension and dimnames is an optional list of dimension names.
As an example, we give the program code, with the help of which a three-dimensional (2×3×4) array of numbers is created.

Statistical programming languages
3. Basic data structures: arrays
>dim1 <- c("A1", "A2")
>dim2 <- c("B1", "B2", "B3")
>dim3 <- c("C1", "C2", "C3", "C4")
>z <- array(1:24, c(2, 3, 4), dimnames=list(dim1, dim2, dim3))
>z
, , C1
B1 B2 B3
A1 1 3 5
A2 2 4 6
, , C2
B1 B2 B3
A1 7 9 11
A2 8 10 12
, , C3
B1 B2 B3
A1 13 15 17
A2 14 16 18
, , C4
B1 B2 B3
A1 19 21 23
A2 20 22 24

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3. Basic data structures: dataframes
A data frame is a more widely used object than a matrix because different columns can contain different types of data (numeric, text, etc.). A data table is the most commonly used data structure in R.
A set of data about patients (table. above) consists of numeric and textual data. This data needs to be represented as a data table, not a matrix, because there are different types of data here.The data table is created using the data function.frame():
mydata < - data.frame(col1, col2, col3,…),
where-col1, col2, col3,... are vectors of any type (textual, numeric, or logical) that will become table columns. Names can be assigned to each column using the names () function. Let's illustrate this with an example of the program code.

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3. Basic data structures: dataframes
patientID <- c(1, 2, 3, 4)
age <- c(25, 34, 28, 52)
diabetes <- c("Type1", "Type2", "Type1", "Type1")
status <- c("Poor", "Improved", "Excellent", "Poor")
patientdata <- data.frame(patientID, age, diabetes, status)
patientdata
patientID age diabetes status
1 1 25 Type1 Poor
2 2 34 Type2 Improved
3 3 28 Type1 Excellent
4 4 52 Type1 Poor

Statistical programming languages
3. Basic data structures: dataframes
Designation of data table elements
>patientdata[1:2]
patientID age
1 25
2 34
3 28
4 52
> patientdata[c("diabetes", "status")]
diabetes status
1 Type1 Poor
2 Type2 Improved
3 Type1 Excellent
patientdata$age [1] 25 34 28 52

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3. Basic data structures: factors
The factor () function stores categorical data as a vector of integers in the range from one to k (where k is the number of unique values of the categorical variable) and as an internal vector of a chain of characters (the original values of the variable) corresponding to these integers.
diabetes <- c("Type1", "Type2", "Type1", "Type1").
diabetes <- factor(diabetes)
Numeric values are assigned in alphabetical order. Any analysis you do with the diabetes vector will take this variable as nominal and choose statistical methods that are appropriate for this type of data.

Statistical programming languages
3. Basic data structures: factors
You can change the default setting by specifying the levels parameter. For example:
>status <- factor(status, order=TRUE,
levels=c("Poor", "Improved", "Excellent"))
will assign levels to the values of the vector as follows:
1=Poor, 2=Improved, 3=Excellent.

Statistical programming languages
3. Basic data structures: factors
The use of factors
>patientID <- c(1, 2, 3, 4) # Enter the data as vectors
>age <- c(25, 34, 28, 52)
diabetes <- c("Type1", "Type2", "Type1", "Type1")
status <- c("Poor", "Improved", "Excellent", "Poor")
diabetes <- factor(diabetes) # we point out that diabetes is a factor
status <- factor(status, order=TRUE) # status – it is an ordered factor
>patientdata <- data.frame(patientID, age, diabetes, status) # combine the data into a table
> str(patientdata)

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3. Basic data structures: lists
Lists are the most complex data type in R. In fact, a list is an ordered list of objects (components). For example, a list can be a combination of vectors, matrices, data tables, and even other lists. The list can be created using the function
list():
mylist <- list(object1, object2, …),
where objects are any data structures we discussed before. Objects in the list can be named:
mylist <- list(name1=object1, name2=object2, …)

Statistical programming languages
3. Basic data structures: lists
Creating a list
>g <- "My First List"
>h <- c(25, 26, 18, 39)
>j <- matrix(1:10, nrow=5)
>k <- c("one", "two", "three")
> mylist <- list(title=g, ages=h, j, k)
> mylist # Display the entire list
mylist[[2]] # Display the second object of the list
> mylist[["ages"]] # Display the second object of the list